cristinadece

cristinadece's Stars

• explosion/spaCy

  💫 Industrial-strength Natural Language Processing (NLP) in Python

  Language:Python29.8k5625.6k4.4k

• EthicalML/awesome-production-machine-learning

  A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

  17.4k405762.2k

• facebookresearch/LASER

  Language-Agnostic SEntence Representations

  Language:Jupyter Notebook3.6k89211460

• giacbrd/ShallowLearn

  An experiment about re-implementing supervised learning models based on shallow neural network approaches (e.g. fastText) with some additional exclusive features and nice API. Written in Python and fully compatible with Scikit-learn.

  Language:Python198182630

• gcunhase/NLPMetrics

  Python code for various NLP metrics

  Language:Jupyter Notebook1677229

• Georgetown-IR-Lab/OpenNIR

  An end-to-end neural ad-hoc ranking pipeline.

  Language:Python14992925

• jingtaozhan/DRhard

  SIGIR'21: Optimizing DR with hard negatives and achieving SOTA first-stage retrieval performance on TREC DL Track.

  Language:Python12642514

• mblondel/svmlight-loader

  Fast and memory-efficient svmlight / libsvm file loader for Python.

  Language:C++11610641

• microsoft/MSMARCO-Conversational-Search

  Truly Conversational Search is the next logic step in the journey to generate intelligent and useful AI. To understand what this may mean, researchers have voiced a continuous desire to study how people currently converse with search engines. Traditionally, the desire to produce such a comprehensive dataset has been limited because those who have this data (Search Engines) have a responsibility to their users to maintain their privacy and cannot share the data publicly in a way that upholds the trusts users have in the Search Engines. Given these two powerful forces we believe we have a dataset and paradigm that meets both sets of needs: A artificial public dataset that approximates the true data and an ability to evaluate model performance on the real user behavior. What this means is we released a public dataset which is generated by creating artificial sessions using embedding similarity and will test on the original data. To say this again: we are not releasing any private user data but are releasing what we believe to be a good representation of true user interactions.

  Language:Python10712421

• hpclab/rankeval

  Official repository of RankEval: An Evaluation and Analysis Framework for Learning-to-Rank Solutions.

  Language:Python88141911

• daltonj/treccastweb

  7711520

• ielab/afirm2019

  Language:Jupyter Notebook399011

• grill-lab/trec-cast-tools

  Tools for the TREC CAsT benchmark

  Language:Python26657

• giacbrd/SmartPipeline

  A framework for rapid development of robust data pipelines following a simple design pattern

  Language:Python23202

• europeana/entity-autocompletion

  DEPRECATED - Entity autocompletion API and relevancy ranking algorithm

  Language:JavaScript62

• hpclab/adaptive-utterance-rewriting-conversational-search

  Official software repository of Mele et al., "Adaptive Utterance Rewriting for Conversational Search", IP&M, 2021.

  Language:Jupyter Notebook5711

• deneb2/crawleb

  A news crawler

  Language:Python2